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Local communities obstruct global consensus: Naming game on multi-local-world networks

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  • Lou, Yang
  • Chen, Guanrong
  • Fan, Zhengping
  • Xiang, Luna

Abstract

Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

Suggested Citation

  • Lou, Yang & Chen, Guanrong & Fan, Zhengping & Xiang, Luna, 2018. "Local communities obstruct global consensus: Naming game on multi-local-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1741-1752.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1741-1752
    DOI: 10.1016/j.physa.2017.11.094
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    Citations

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    Cited by:

    1. Zhou, Jianfeng & Lou, Yang & Chen, Guanrong & Tang, Wallace K.S., 2018. "Multi-language naming game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 620-634.
    2. Fan, Zhong-Yan & Lai, Ying-Cheng & Tang, Wallace Kit-Sang, 2020. "Likelihood category game model for knowledge consensus," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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